Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations1414
Missing cells913
Missing cells (%)3.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory210.0 KiB
Average record size in memory152.1 B

Variable types

Text6
DateTime2
Numeric7
Categorical4

Alerts

Channels is highly overall correlated with Fulfillment Type and 3 other fieldsHigh correlation
Fulfillment Type is highly overall correlated with ChannelsHigh correlation
Item Options Total Price is highly overall correlated with Channels and 2 other fieldsHigh correlation
Item Price is highly overall correlated with Channels and 2 other fieldsHigh correlation
Item Total Price is highly overall correlated with Channels and 2 other fieldsHigh correlation
Order Subtotal is highly overall correlated with Order Tax Total and 1 other fieldsHigh correlation
Order Tax Total is highly overall correlated with Order Subtotal and 1 other fieldsHigh correlation
Order Total is highly overall correlated with Order Subtotal and 1 other fieldsHigh correlation
Fulfillment Type is highly imbalanced (64.6%) Imbalance
Fulfillment Status is highly imbalanced (97.2%) Imbalance
Recipient Email has 341 (24.1%) missing values Missing
Item Modifiers has 569 (40.2%) missing values Missing
Item Quantity is highly skewed (γ1 = 34.18682456) Skewed
Item Price has 15 (1.1%) zeros Zeros
Item Options Total Price has 15 (1.1%) zeros Zeros
Item Total Price has 15 (1.1%) zeros Zeros

Reproduction

Analysis started2024-12-21 02:09:31.009441
Analysis finished2024-12-21 02:09:38.359732
Duration7.35 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Order
Text

Distinct262
Distinct (%)18.5%
Missing1
Missing (%)0.1%
Memory size11.2 KiB
2024-12-20T18:09:38.942774image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length24
Median length23
Mean length15.929936
Min length8

Characters and Unicode

Total characters22509
Distinct characters44
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)4.2%

Sample

1st rowUber Eats Delivery A97C3
2nd rowUber Eats Delivery 86B6C
3rd rowUber Eats Delivery 86B6C
4th rowUber Eats Delivery 1B73A
5th rowUber Eats Delivery 1B73A
ValueCountFrequency (%)
doordash 649
21.3%
square 361
11.8%
online 361
11.8%
delivery 317
10.4%
uber 192
 
6.3%
eats 192
 
6.3%
postmates 149
 
4.9%
payment 41
 
1.3%
link 41
 
1.3%
pickup 24
 
0.8%
Other values (260) 723
23.7%
2024-12-20T18:09:39.392409image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1738
 
7.7%
D 1733
 
7.7%
O 1701
 
7.6%
1637
 
7.3%
S 1031
 
4.6%
r 870
 
3.9%
n 804
 
3.6%
A 757
 
3.4%
a 743
 
3.3%
i 743
 
3.3%
Other values (34) 10752
47.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 22509
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1738
 
7.7%
D 1733
 
7.7%
O 1701
 
7.6%
1637
 
7.3%
S 1031
 
4.6%
r 870
 
3.9%
n 804
 
3.6%
A 757
 
3.4%
a 743
 
3.3%
i 743
 
3.3%
Other values (34) 10752
47.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 22509
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1738
 
7.7%
D 1733
 
7.7%
O 1701
 
7.6%
1637
 
7.3%
S 1031
 
4.6%
r 870
 
3.9%
n 804
 
3.6%
A 757
 
3.4%
a 743
 
3.3%
i 743
 
3.3%
Other values (34) 10752
47.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 22509
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1738
 
7.7%
D 1733
 
7.7%
O 1701
 
7.6%
1637
 
7.3%
S 1031
 
4.6%
r 870
 
3.9%
n 804
 
3.6%
A 757
 
3.4%
a 743
 
3.3%
i 743
 
3.3%
Other values (34) 10752
47.8%
Distinct214
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
Minimum2023-12-28 00:00:00
Maximum2024-12-20 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2024-12-20T18:09:39.581832image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:39.809270image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Order Subtotal
Real number (ℝ)

High correlation 

Distinct408
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.560276
Minimum5.64
Maximum1039.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2024-12-20T18:09:39.983804image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum5.64
5-th percentile19.69
Q135.92
median51.55
Q374.6
95-th percentile172.6545
Maximum1039.95
Range1034.31
Interquartile range (IQR)38.68

Descriptive statistics

Standard deviation80.340354
Coefficient of variation (CV)1.1549746
Kurtosis64.593764
Mean69.560276
Median Absolute Deviation (MAD)17.09
Skewness6.6472079
Sum98358.23
Variance6454.5724
MonotonicityNot monotonic
2024-12-20T18:09:40.142978image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
103.15 15
 
1.1%
205.01 12
 
0.8%
19.99 12
 
0.8%
175.44 11
 
0.8%
44.27 10
 
0.7%
57.86 10
 
0.7%
98.22 10
 
0.7%
93.28 10
 
0.7%
461.07 10
 
0.7%
58.04 10
 
0.7%
Other values (398) 1304
92.2%
ValueCountFrequency (%)
5.64 2
0.1%
8.49 1
 
0.1%
8.79 1
 
0.1%
9.24 1
 
0.1%
9.34 1
 
0.1%
9.48 1
 
0.1%
11.19 1
 
0.1%
11.22 1
 
0.1%
11.92 2
0.1%
13.32 4
0.3%
ValueCountFrequency (%)
1039.95 4
 
0.3%
461.07 10
0.7%
450 1
 
0.1%
444.85 2
 
0.1%
358.35 9
0.6%
357.98 2
 
0.1%
318.67 8
0.6%
259.61 1
 
0.1%
211.26 3
 
0.2%
205.01 12
0.8%

Order Tax Total
Real number (ℝ)

High correlation 

Distinct336
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3607072
Minimum0.54
Maximum98.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2024-12-20T18:09:40.306182image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0.54
5-th percentile1.85
Q13.03
median4.6
Q36.79
95-th percentile16.08
Maximum98.8
Range98.26
Interquartile range (IQR)3.76

Descriptive statistics

Standard deviation7.6357162
Coefficient of variation (CV)1.2004508
Kurtosis65.31483
Mean6.3607072
Median Absolute Deviation (MAD)1.71
Skewness6.7060053
Sum8994.04
Variance58.304162
MonotonicityNot monotonic
2024-12-20T18:09:40.464374image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.46 19
 
1.3%
3.32 15
 
1.1%
9.32 15
 
1.1%
5.23 13
 
0.9%
2.94 12
 
0.8%
8.09 12
 
0.8%
17.86 12
 
0.8%
16.38 11
 
0.8%
5.04 10
 
0.7%
43.8 10
 
0.7%
Other values (326) 1285
90.9%
ValueCountFrequency (%)
0.54 2
0.1%
0.81 1
0.1%
0.84 1
0.1%
0.88 1
0.1%
0.89 1
0.1%
0.9 1
0.1%
1.06 1
0.1%
1.07 1
0.1%
1.13 2
0.1%
1.19 2
0.1%
ValueCountFrequency (%)
98.8 4
 
0.3%
43.8 10
0.7%
42.75 1
 
0.1%
42.26 2
 
0.1%
34.04 9
0.6%
34.01 2
 
0.1%
30.27 8
0.6%
24.19 1
 
0.1%
20.07 3
 
0.2%
17.86 12
0.8%

Order Total
Real number (ℝ)

High correlation 

Distinct438
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.828868
Minimum6.18
Maximum1158.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2024-12-20T18:09:40.612256image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum6.18
5-th percentile21.89
Q138.92
median58.18
Q383.28
95-th percentile190.858
Maximum1158.75
Range1152.57
Interquartile range (IQR)44.36

Descriptive statistics

Standard deviation90.142546
Coefficient of variation (CV)1.1582148
Kurtosis62.701077
Mean77.828868
Median Absolute Deviation (MAD)20.13
Skewness6.5368083
Sum110050.02
Variance8125.6787
MonotonicityNot monotonic
2024-12-20T18:09:40.761543image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
112.47 15
 
1.1%
243.37 12
 
0.8%
191.82 11
 
0.8%
21.89 10
 
0.7%
63.08 10
 
0.7%
504.87 10
 
0.7%
101.1 10
 
0.7%
48.48 10
 
0.7%
107.08 10
 
0.7%
42.95 9
 
0.6%
Other values (428) 1307
92.4%
ValueCountFrequency (%)
6.18 2
0.1%
9.3 1
 
0.1%
9.63 1
 
0.1%
10.12 1
 
0.1%
10.23 1
 
0.1%
10.38 1
 
0.1%
12.25 1
 
0.1%
13.41 1
 
0.1%
14.59 4
0.3%
15.71 1
 
0.1%
ValueCountFrequency (%)
1158.75 4
 
0.3%
512.75 1
 
0.1%
504.87 10
0.7%
487.11 2
 
0.1%
427.79 2
 
0.1%
412.01 9
0.6%
363.94 8
0.6%
283.8 1
 
0.1%
252.46 3
 
0.2%
243.37 12
0.8%
Distinct515
Distinct (%)36.4%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
Minimum2023-12-28 13:30:00
Maximum2024-12-20 13:25:00
Invalid dates0
Invalid dates (%)0.0%
2024-12-20T18:09:40.917102image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:41.151885image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Fulfillment Type
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
Pickup
1246 
Curbside
 
79
Delivery
 
47
Other
 
42

Length

Max length8
Median length6
Mean length6.1485149
Min length5

Characters and Unicode

Total characters8694
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPickup
2nd rowPickup
3rd rowPickup
4th rowPickup
5th rowPickup

Common Values

ValueCountFrequency (%)
Pickup 1246
88.1%
Curbside 79
 
5.6%
Delivery 47
 
3.3%
Other 42
 
3.0%

Length

2024-12-20T18:09:41.351262image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-20T18:09:41.517711image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
pickup 1246
88.1%
curbside 79
 
5.6%
delivery 47
 
3.3%
other 42
 
3.0%

Most occurring characters

ValueCountFrequency (%)
i 1372
15.8%
u 1325
15.2%
P 1246
14.3%
c 1246
14.3%
k 1246
14.3%
p 1246
14.3%
e 215
 
2.5%
r 168
 
1.9%
C 79
 
0.9%
s 79
 
0.9%
Other values (9) 472
 
5.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8694
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1372
15.8%
u 1325
15.2%
P 1246
14.3%
c 1246
14.3%
k 1246
14.3%
p 1246
14.3%
e 215
 
2.5%
r 168
 
1.9%
C 79
 
0.9%
s 79
 
0.9%
Other values (9) 472
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8694
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1372
15.8%
u 1325
15.2%
P 1246
14.3%
c 1246
14.3%
k 1246
14.3%
p 1246
14.3%
e 215
 
2.5%
r 168
 
1.9%
C 79
 
0.9%
s 79
 
0.9%
Other values (9) 472
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8694
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1372
15.8%
u 1325
15.2%
P 1246
14.3%
c 1246
14.3%
k 1246
14.3%
p 1246
14.3%
e 215
 
2.5%
r 168
 
1.9%
C 79
 
0.9%
s 79
 
0.9%
Other values (9) 472
 
5.4%

Fulfillment Status
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
Completed
1410 
Canceled
 
4

Length

Max length9
Median length9
Mean length8.9971711
Min length8

Characters and Unicode

Total characters12722
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCompleted
2nd rowCompleted
3rd rowCompleted
4th rowCompleted
5th rowCompleted

Common Values

ValueCountFrequency (%)
Completed 1410
99.7%
Canceled 4
 
0.3%

Length

2024-12-20T18:09:41.693721image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-20T18:09:41.814286image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
completed 1410
99.7%
canceled 4
 
0.3%

Most occurring characters

ValueCountFrequency (%)
e 2828
22.2%
l 1414
11.1%
C 1414
11.1%
d 1414
11.1%
o 1410
11.1%
m 1410
11.1%
p 1410
11.1%
t 1410
11.1%
a 4
 
< 0.1%
n 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12722
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2828
22.2%
l 1414
11.1%
C 1414
11.1%
d 1414
11.1%
o 1410
11.1%
m 1410
11.1%
p 1410
11.1%
t 1410
11.1%
a 4
 
< 0.1%
n 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12722
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2828
22.2%
l 1414
11.1%
C 1414
11.1%
d 1414
11.1%
o 1410
11.1%
m 1410
11.1%
p 1410
11.1%
t 1410
11.1%
a 4
 
< 0.1%
n 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12722
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2828
22.2%
l 1414
11.1%
C 1414
11.1%
d 1414
11.1%
o 1410
11.1%
m 1410
11.1%
p 1410
11.1%
t 1410
11.1%
a 4
 
< 0.1%
n 4
 
< 0.1%

Channels
Categorical

High correlation 

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
DoorDash
670 
BELLY RUBB | BBQ Catering | Barbecue To Go and Delivery
361 
Postmates Delivery
341 
Payment Links
 
41
Belly Rubb
 
1

Length

Max length55
Median length18
Mean length22.557284
Min length8

Characters and Unicode

Total characters31896
Distinct characters33
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st rowPostmates Delivery
2nd rowPostmates Delivery
3rd rowPostmates Delivery
4th rowPostmates Delivery
5th rowPostmates Delivery

Common Values

ValueCountFrequency (%)
DoorDash 670
47.4%
BELLY RUBB | BBQ Catering | Barbecue To Go and Delivery 361
25.5%
Postmates Delivery 341
24.1%
Payment Links 41
 
2.9%
Belly Rubb 1
 
0.1%

Length

2024-12-20T18:09:41.951949image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-20T18:09:42.074333image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
722
13.4%
delivery 702
13.0%
doordash 670
12.4%
belly 362
6.7%
rubb 362
6.7%
bbq 361
6.7%
catering 361
6.7%
barbecue 361
6.7%
go 361
6.7%
to 361
6.7%
Other values (4) 784
14.5%

Most occurring characters

ValueCountFrequency (%)
3993
 
12.5%
e 2870
 
9.0%
o 2403
 
7.5%
B 2167
 
6.8%
a 2135
 
6.7%
r 2094
 
6.6%
D 2042
 
6.4%
s 1393
 
4.4%
i 1104
 
3.5%
t 1084
 
3.4%
Other values (23) 10611
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31896
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3993
 
12.5%
e 2870
 
9.0%
o 2403
 
7.5%
B 2167
 
6.8%
a 2135
 
6.7%
r 2094
 
6.6%
D 2042
 
6.4%
s 1393
 
4.4%
i 1104
 
3.5%
t 1084
 
3.4%
Other values (23) 10611
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31896
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3993
 
12.5%
e 2870
 
9.0%
o 2403
 
7.5%
B 2167
 
6.8%
a 2135
 
6.7%
r 2094
 
6.6%
D 2042
 
6.4%
s 1393
 
4.4%
i 1104
 
3.5%
t 1084
 
3.4%
Other values (23) 10611
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31896
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3993
 
12.5%
e 2870
 
9.0%
o 2403
 
7.5%
B 2167
 
6.8%
a 2135
 
6.7%
r 2094
 
6.6%
D 2042
 
6.4%
s 1393
 
4.4%
i 1104
 
3.5%
t 1084
 
3.4%
Other values (23) 10611
33.3%
Distinct372
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
2024-12-20T18:09:42.296193image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length23
Median length18
Mean length11.155587
Min length4

Characters and Unicode

Total characters15774
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique72 ?
Unique (%)5.1%

Sample

1st rowA97C3-Olga G.
2nd row86B6C-Martin T.
3rd row86B6C-Martin T.
4th row1B73A-Diana O.
5th row1B73A-Diana O.
ValueCountFrequency (%)
s 127
 
4.5%
k 71
 
2.5%
d 71
 
2.5%
b 71
 
2.5%
c 66
 
2.3%
a 66
 
2.3%
m 66
 
2.3%
o 65
 
2.3%
g 55
 
1.9%
t 54
 
1.9%
Other values (441) 2132
75.0%
2024-12-20T18:09:42.712481image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1430
 
9.1%
a 1337
 
8.5%
e 1069
 
6.8%
n 971
 
6.2%
i 734
 
4.7%
r 714
 
4.5%
o 544
 
3.4%
l 487
 
3.1%
- 391
 
2.5%
A 385
 
2.4%
Other values (54) 7712
48.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 15774
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1430
 
9.1%
a 1337
 
8.5%
e 1069
 
6.8%
n 971
 
6.2%
i 734
 
4.7%
r 714
 
4.5%
o 544
 
3.4%
l 487
 
3.1%
- 391
 
2.5%
A 385
 
2.4%
Other values (54) 7712
48.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 15774
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1430
 
9.1%
a 1337
 
8.5%
e 1069
 
6.8%
n 971
 
6.2%
i 734
 
4.7%
r 714
 
4.5%
o 544
 
3.4%
l 487
 
3.1%
- 391
 
2.5%
A 385
 
2.4%
Other values (54) 7712
48.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 15774
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1430
 
9.1%
a 1337
 
8.5%
e 1069
 
6.8%
n 971
 
6.2%
i 734
 
4.7%
r 714
 
4.5%
o 544
 
3.4%
l 487
 
3.1%
- 391
 
2.5%
A 385
 
2.4%
Other values (54) 7712
48.9%

Recipient Email
Text

Missing 

Distinct98
Distinct (%)9.1%
Missing341
Missing (%)24.1%
Memory size11.2 KiB
2024-12-20T18:09:42.896368image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length38
Median length38
Mean length31.955266
Min length13

Characters and Unicode

Total characters34288
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)2.0%

Sample

1st rowriplawlavc@gmail.com
2nd rowriplawlavc@gmail.com
3rd rowpoint-of-sale-integration@doordash.com
4th rowpoint-of-sale-integration@doordash.com
5th rowpoint-of-sale-integration@doordash.com
ValueCountFrequency (%)
point-of-sale-integration@doordash.com 670
62.4%
alex.anthony.diaz@gmail.com 31
 
2.9%
mogshut@gmail.com 28
 
2.6%
steven.trella@gmail.com 28
 
2.6%
bidium@gmail.com 22
 
2.1%
monalapides@gmail.com 12
 
1.1%
m.m.keshishyan@gmail.com 11
 
1.0%
strwbrytiff@yahoo.com 10
 
0.9%
kennysanchez5122@yahoo.com 10
 
0.9%
narumol2003@yahoo.com 8
 
0.7%
Other values (88) 243
 
22.6%
2024-12-20T18:09:43.272388image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 4783
13.9%
a 2892
 
8.4%
i 2611
 
7.6%
n 2400
 
7.0%
t 2292
 
6.7%
- 2010
 
5.9%
e 1791
 
5.2%
r 1620
 
4.7%
m 1553
 
4.5%
s 1552
 
4.5%
Other values (45) 10784
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34288
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 4783
13.9%
a 2892
 
8.4%
i 2611
 
7.6%
n 2400
 
7.0%
t 2292
 
6.7%
- 2010
 
5.9%
e 1791
 
5.2%
r 1620
 
4.7%
m 1553
 
4.5%
s 1552
 
4.5%
Other values (45) 10784
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34288
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 4783
13.9%
a 2892
 
8.4%
i 2611
 
7.6%
n 2400
 
7.0%
t 2292
 
6.7%
- 2010
 
5.9%
e 1791
 
5.2%
r 1620
 
4.7%
m 1553
 
4.5%
s 1552
 
4.5%
Other values (45) 10784
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34288
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 4783
13.9%
a 2892
 
8.4%
i 2611
 
7.6%
n 2400
 
7.0%
t 2292
 
6.7%
- 2010
 
5.9%
e 1791
 
5.2%
r 1620
 
4.7%
m 1553
 
4.5%
s 1552
 
4.5%
Other values (45) 10784
31.5%
Distinct96
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size11.2 KiB
2024-12-20T18:09:43.482047image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length15
Median length12
Mean length11.826733
Min length10

Characters and Unicode

Total characters16723
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)1.3%

Sample

1st row+1 312-766-6835
2nd row+1 312-766-6835
3rd row+1 312-766-6835
4th row+1 312-766-6835
5th row+1 312-766-6835
ValueCountFrequency (%)
1 365
20.5%
8552228111 359
20.2%
312-766-6835 341
19.2%
8559731040 311
17.5%
16268647315 35
 
2.0%
16266164211 31
 
1.7%
18184862439 28
 
1.6%
818-822-5060 22
 
1.2%
18186360644 12
 
0.7%
17472562597 11
 
0.6%
Other values (87) 264
14.8%
2024-12-20T18:09:43.829458image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3008
18.0%
5 2006
12.0%
8 2000
12.0%
2 1883
11.3%
6 1657
9.9%
3 1320
7.9%
7 945
 
5.7%
0 862
 
5.2%
+ 744
 
4.4%
- 730
 
4.4%
Other values (3) 1568
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 16723
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 3008
18.0%
5 2006
12.0%
8 2000
12.0%
2 1883
11.3%
6 1657
9.9%
3 1320
7.9%
7 945
 
5.7%
0 862
 
5.2%
+ 744
 
4.4%
- 730
 
4.4%
Other values (3) 1568
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 16723
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 3008
18.0%
5 2006
12.0%
8 2000
12.0%
2 1883
11.3%
6 1657
9.9%
3 1320
7.9%
7 945
 
5.7%
0 862
 
5.2%
+ 744
 
4.4%
- 730
 
4.4%
Other values (3) 1568
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 16723
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 3008
18.0%
5 2006
12.0%
8 2000
12.0%
2 1883
11.3%
6 1657
9.9%
3 1320
7.9%
7 945
 
5.7%
0 862
 
5.2%
+ 744
 
4.4%
- 730
 
4.4%
Other values (3) 1568
9.4%

Item Quantity
Real number (ℝ)

Skewed 

Distinct13
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2743989
Minimum1
Maximum130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2024-12-20T18:09:43.961242image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum130
Range129
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.5416372
Coefficient of variation (CV)2.7790649
Kurtosis1237.8686
Mean1.2743989
Median Absolute Deviation (MAD)0
Skewness34.186825
Sum1802
Variance12.543194
MonotonicityNot monotonic
2024-12-20T18:09:44.095384image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 1278
90.4%
2 96
 
6.8%
3 13
 
0.9%
4 13
 
0.9%
5 4
 
0.3%
15 2
 
0.1%
6 2
 
0.1%
8 1
 
0.1%
11 1
 
0.1%
14 1
 
0.1%
Other values (3) 3
 
0.2%
ValueCountFrequency (%)
1 1278
90.4%
2 96
 
6.8%
3 13
 
0.9%
4 13
 
0.9%
5 4
 
0.3%
6 2
 
0.1%
7 1
 
0.1%
8 1
 
0.1%
9 1
 
0.1%
11 1
 
0.1%
ValueCountFrequency (%)
130 1
 
0.1%
15 2
 
0.1%
14 1
 
0.1%
11 1
 
0.1%
9 1
 
0.1%
8 1
 
0.1%
7 1
 
0.1%
6 2
 
0.1%
5 4
 
0.3%
4 13
0.9%
Distinct75
Distinct (%)5.3%
Missing1
Missing (%)0.1%
Memory size11.2 KiB
2024-12-20T18:09:44.305119image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length31
Median length25
Mean length17.627035
Min length7

Characters and Unicode

Total characters24907
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)0.8%

Sample

1st rowBEEF BACK RIBS (Full Rack)
2nd rowLOADED FRIES
3rd rowCHICKEN WINGS
4th rowCIABATTA STEAK SANDWICH
5th rowLOADED FRIES
ValueCountFrequency (%)
back 289
 
6.7%
baby 281
 
6.5%
ribs 222
 
5.1%
glazed 166
 
3.8%
pork 166
 
3.8%
belly 166
 
3.8%
fries 150
 
3.5%
combo 144
 
3.3%
rib 139
 
3.2%
beef 120
 
2.8%
Other values (100) 2475
57.3%
2024-12-20T18:09:44.665258image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2905
 
11.7%
E 2284
 
9.2%
B 1938
 
7.8%
A 1878
 
7.5%
S 1505
 
6.0%
R 1427
 
5.7%
I 1423
 
5.7%
C 1410
 
5.7%
L 1121
 
4.5%
O 985
 
4.0%
Other values (55) 8031
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 24907
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2905
 
11.7%
E 2284
 
9.2%
B 1938
 
7.8%
A 1878
 
7.5%
S 1505
 
6.0%
R 1427
 
5.7%
I 1423
 
5.7%
C 1410
 
5.7%
L 1121
 
4.5%
O 985
 
4.0%
Other values (55) 8031
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 24907
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2905
 
11.7%
E 2284
 
9.2%
B 1938
 
7.8%
A 1878
 
7.5%
S 1505
 
6.0%
R 1427
 
5.7%
I 1423
 
5.7%
C 1410
 
5.7%
L 1121
 
4.5%
O 985
 
4.0%
Other values (55) 8031
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 24907
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2905
 
11.7%
E 2284
 
9.2%
B 1938
 
7.8%
A 1878
 
7.5%
S 1505
 
6.0%
R 1427
 
5.7%
I 1423
 
5.7%
C 1410
 
5.7%
L 1121
 
4.5%
O 985
 
4.0%
Other values (55) 8031
32.2%

Item Variation
Categorical

Distinct25
Distinct (%)1.8%
Missing1
Missing (%)0.1%
Memory size11.2 KiB
Regular
727 
Full Rack
129 
Side
116 
Full
103 
4 Bites
80 
Other values (20)
258 

Length

Max length39
Median length7
Mean length6.9129512
Min length4

Characters and Unicode

Total characters9768
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st rowRegular
2nd rowRegular
3rd row6 pcs
4th rowRegular
5th rowRegular

Common Values

ValueCountFrequency (%)
Regular 727
51.4%
Full Rack 129
 
9.1%
Side 116
 
8.2%
Full 103
 
7.3%
4 Bites 80
 
5.7%
6 pcs 53
 
3.7%
Half rack 53
 
3.7%
8 pcs 26
 
1.8%
12 pcs 25
 
1.8%
2 sliders 23
 
1.6%
Other values (15) 78
 
5.5%

Length

2024-12-20T18:09:44.883219image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
regular 727
38.2%
full 239
 
12.6%
rack 182
 
9.6%
side 130
 
6.8%
pcs 122
 
6.4%
bites 101
 
5.3%
4 90
 
4.7%
6 60
 
3.2%
half 55
 
2.9%
sliders 35
 
1.8%
Other values (13) 163
 
8.6%

Most occurring characters

ValueCountFrequency (%)
l 1320
13.5%
e 1049
10.7%
a 992
10.2%
u 978
10.0%
R 869
8.9%
r 855
8.8%
g 727
 
7.4%
491
 
5.0%
s 318
 
3.3%
c 305
 
3.1%
Other values (29) 1864
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9768
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 1320
13.5%
e 1049
10.7%
a 992
10.2%
u 978
10.0%
R 869
8.9%
r 855
8.8%
g 727
 
7.4%
491
 
5.0%
s 318
 
3.3%
c 305
 
3.1%
Other values (29) 1864
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9768
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 1320
13.5%
e 1049
10.7%
a 992
10.2%
u 978
10.0%
R 869
8.9%
r 855
8.8%
g 727
 
7.4%
491
 
5.0%
s 318
 
3.3%
c 305
 
3.1%
Other values (29) 1864
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9768
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 1320
13.5%
e 1049
10.7%
a 992
10.2%
u 978
10.0%
R 869
8.9%
r 855
8.8%
g 727
 
7.4%
491
 
5.0%
s 318
 
3.3%
c 305
 
3.1%
Other values (29) 1864
19.1%

Item Modifiers
Text

Missing 

Distinct397
Distinct (%)47.0%
Missing569
Missing (%)40.2%
Memory size11.2 KiB
2024-12-20T18:09:45.118252image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Length

Max length236
Median length148
Mean length56.405917
Min length11

Characters and Unicode

Total characters47663
Distinct characters60
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique304 ?
Unique (%)36.0%

Sample

1st row1 x Signature BBQ Glaze
2nd row1 x No Cheddar, 1 x No Provolone, 1 x Signature BBQ Sauce Drizzle, 1 x Sweet and Spicy BBQ Sauce Dip, 1 x Lemon Pepper, 1 x Add Pork Rib Meat (Of-The-Bone!)
3rd row1 x Sweet&Spicy glaze (Pairs well w/ LemonPeeper seasoning), 1 x Zesty Lemon Pepper
4th row1 x Blue Cheese Sauce Drizzle, 1 x Salt and Pepper
5th row1 x Sweet&Spicy Glaze, 1 x Please, cut it!
ValueCountFrequency (%)
1 1951
19.2%
x 1951
19.2%
bbq 524
 
5.2%
glaze 459
 
4.5%
signature 424
 
4.2%
sauce 294
 
2.9%
dip 287
 
2.8%
pepper 262
 
2.6%
no 179
 
1.8%
salt 143
 
1.4%
Other values (112) 3680
36.2%
2024-12-20T18:09:45.553316image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9309
19.5%
e 4616
 
9.7%
a 2912
 
6.1%
x 2000
 
4.2%
1 1951
 
4.1%
p 1681
 
3.5%
l 1588
 
3.3%
i 1576
 
3.3%
r 1571
 
3.3%
S 1420
 
3.0%
Other values (50) 19039
39.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 47663
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9309
19.5%
e 4616
 
9.7%
a 2912
 
6.1%
x 2000
 
4.2%
1 1951
 
4.1%
p 1681
 
3.5%
l 1588
 
3.3%
i 1576
 
3.3%
r 1571
 
3.3%
S 1420
 
3.0%
Other values (50) 19039
39.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 47663
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9309
19.5%
e 4616
 
9.7%
a 2912
 
6.1%
x 2000
 
4.2%
1 1951
 
4.1%
p 1681
 
3.5%
l 1588
 
3.3%
i 1576
 
3.3%
r 1571
 
3.3%
S 1420
 
3.0%
Other values (50) 19039
39.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 47663
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9309
19.5%
e 4616
 
9.7%
a 2912
 
6.1%
x 2000
 
4.2%
1 1951
 
4.1%
p 1681
 
3.5%
l 1588
 
3.3%
i 1576
 
3.3%
r 1571
 
3.3%
S 1420
 
3.0%
Other values (50) 19039
39.9%

Item Price
Real number (ℝ)

High correlation  Zeros 

Distinct80
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.26157
Minimum0
Maximum450
Zeros15
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2024-12-20T18:09:45.711016image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.99
Q15.64
median9.34
Q323.99
95-th percentile48.99
Maximum450
Range450
Interquartile range (IQR)18.35

Descriptive statistics

Standard deviation23.437416
Coefficient of variation (CV)1.3577801
Kurtosis109.32777
Mean17.26157
Median Absolute Deviation (MAD)7.12
Skewness7.9891213
Sum24407.86
Variance549.31248
MonotonicityNot monotonic
2024-12-20T18:09:45.862542image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.93 129
 
9.1%
8.49 127
 
9.0%
3.99 81
 
5.7%
0.99 65
 
4.6%
27.49 63
 
4.5%
23.99 53
 
3.7%
48.99 48
 
3.4%
7.25 48
 
3.4%
6.99 43
 
3.0%
9.34 42
 
3.0%
Other values (70) 715
50.6%
ValueCountFrequency (%)
0 15
 
1.1%
0.5 5
 
0.4%
0.85 6
 
0.4%
0.95 17
 
1.2%
0.99 65
4.6%
1.45 14
 
1.0%
1.5 1
 
0.1%
1.99 36
2.5%
2.49 4
 
0.3%
2.64 3
 
0.2%
ValueCountFrequency (%)
450 1
 
0.1%
259.61 1
 
0.1%
257 2
 
0.1%
162 3
 
0.2%
127 2
 
0.1%
117 8
0.6%
100 1
 
0.1%
67.99 1
 
0.1%
60 1
 
0.1%
57.99 3
 
0.2%

Item Options Total Price
Real number (ℝ)

High correlation  Zeros 

Distinct235
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.857956
Minimum0
Maximum450
Zeros15
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2024-12-20T18:09:46.012033image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.99
Q16.29
median11.35
Q323.99
95-th percentile48.99
Maximum450
Range450
Interquartile range (IQR)17.7

Descriptive statistics

Standard deviation23.530961
Coefficient of variation (CV)1.3176738
Kurtosis107.26482
Mean17.857956
Median Absolute Deviation (MAD)7.36
Skewness7.8893298
Sum25251.15
Variance553.70611
MonotonicityNot monotonic
2024-12-20T18:09:46.174555image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.49 96
 
6.8%
3.99 81
 
5.7%
0.99 65
 
4.6%
34.93 64
 
4.5%
48.99 48
 
3.4%
27.49 45
 
3.2%
9.34 40
 
2.8%
1.99 36
 
2.5%
23.99 34
 
2.4%
4.99 32
 
2.3%
Other values (225) 873
61.7%
ValueCountFrequency (%)
0 15
 
1.1%
0.5 5
 
0.4%
0.85 6
 
0.4%
0.95 17
 
1.2%
0.99 65
4.6%
1.45 14
 
1.0%
1.5 1
 
0.1%
1.99 36
2.5%
2.49 4
 
0.3%
2.64 3
 
0.2%
ValueCountFrequency (%)
450 1
 
0.1%
260 1
 
0.1%
259.61 1
 
0.1%
257 1
 
0.1%
162 3
0.2%
127 2
 
0.1%
120 3
0.2%
117 5
0.4%
100 1
 
0.1%
67.99 1
 
0.1%

Item Total Price
Real number (ℝ)

High correlation  Zeros 

Distinct602
Distinct (%)42.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.978006
Minimum0
Maximum567.98
Zeros15
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size11.2 KiB
2024-12-20T18:09:46.330306image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.08
Q17.65
median13.315
Q327.35
95-th percentile53.65
Maximum567.98
Range567.98
Interquartile range (IQR)19.7

Descriptive statistics

Standard deviation33.748736
Coefficient of variation (CV)1.5355686
Kurtosis101.88681
Mean21.978006
Median Absolute Deviation (MAD)8.925
Skewness8.2887236
Sum31076.9
Variance1138.9772
MonotonicityNot monotonic
2024-12-20T18:09:46.489708image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.37 48
 
3.4%
1.08 36
 
2.5%
9.3 35
 
2.5%
10.23 33
 
2.3%
38.25 31
 
2.2%
26.27 21
 
1.5%
7.94 18
 
1.3%
19.11 17
 
1.2%
53.64 17
 
1.2%
25.68 16
 
1.1%
Other values (592) 1142
80.8%
ValueCountFrequency (%)
0 15
1.1%
0.54 2
 
0.1%
0.55 3
 
0.2%
0.91 2
 
0.1%
0.93 2
 
0.1%
0.97 1
 
0.1%
0.99 2
 
0.1%
1.02 3
 
0.2%
1.03 3
 
0.2%
1.04 7
0.5%
ValueCountFrequency (%)
567.98 1
0.1%
492.75 1
0.1%
438 1
0.1%
284.7 1
0.1%
283.8 1
0.1%
281.41 1
0.1%
267.74 1
0.1%
229.49 1
0.1%
177.39 2
0.1%
176.92 1
0.1%

Interactions

2024-12-20T18:09:36.835828image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:31.665822image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:32.484088image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:33.425467image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:34.253926image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:35.124419image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:35.850231image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:36.956507image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:31.776439image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:32.634357image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:33.540810image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:34.412689image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:35.241634image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:35.977758image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:37.067983image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:31.899561image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:32.749833image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:33.652963image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:34.537758image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:35.342309image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:36.133954image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:37.181983image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:32.013292image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:32.876573image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:33.770176image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:34.677705image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:35.444577image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:36.272368image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:37.295372image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:32.129921image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:33.009227image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:33.894230image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:34.794074image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:35.546072image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:36.404625image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:37.419510image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:32.236569image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:33.154491image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:33.997908image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:34.905918image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:35.644979image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:36.529400image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:37.518923image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:32.341253image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:33.291022image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:34.103146image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:35.009447image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:35.742146image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2024-12-20T18:09:36.701435image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2024-12-20T18:09:46.636818image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ChannelsFulfillment StatusFulfillment TypeItem Options Total PriceItem PriceItem QuantityItem Total PriceItem VariationOrder SubtotalOrder Tax TotalOrder Total
Channels1.0000.0740.6530.5010.5010.1570.5260.1490.4280.4280.434
Fulfillment Status0.0741.0000.2840.0000.0000.0000.0000.0000.0000.0000.000
Fulfillment Type0.6530.2841.0000.1090.1090.1560.2250.1190.4720.4720.475
Item Options Total Price0.5010.0000.1091.0000.987-0.1400.9630.0680.0650.0740.069
Item Price0.5010.0000.1090.9871.000-0.1330.9540.0680.0690.0780.073
Item Quantity0.1570.0000.156-0.140-0.1331.0000.0690.0000.2290.2280.229
Item Total Price0.5260.0000.2250.9630.9540.0691.0000.0000.1200.1320.124
Item Variation0.1490.0000.1190.0680.0680.0000.0001.0000.0370.0370.048
Order Subtotal0.4280.0000.4720.0650.0690.2290.1200.0371.0000.9890.996
Order Tax Total0.4280.0000.4720.0740.0780.2280.1320.0370.9891.0000.989
Order Total0.4340.0000.4750.0690.0730.2290.1240.0480.9960.9891.000

Missing values

2024-12-20T18:09:37.680701image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-20T18:09:38.050261image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-20T18:09:38.270154image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

OrderOrder DateOrder SubtotalOrder Tax TotalOrder TotalFulfillment DateFulfillment TypeFulfillment StatusChannelsRecipient NameRecipient EmailRecipient PhoneItem QuantityItem NameItem VariationItem ModifiersItem PriceItem Options Total PriceItem Total Price
0Uber Eats Delivery A97C32024-12-2048.994.6553.642024-12-20 13:25:00PickupCompletedPostmates DeliveryA97C3-Olga G.NaN+1 312-766-68351BEEF BACK RIBS (Full Rack)Regular1 x Signature BBQ Glaze48.9948.9953.64
1Uber Eats Delivery 86B6C2024-12-1940.123.8143.932024-12-19 18:40:00PickupCompletedPostmates Delivery86B6C-Martin T.NaN+1 312-766-68351LOADED FRIESRegular1 x No Cheddar, 1 x No Provolone, 1 x Signature BBQ Sauce Drizzle, 1 x Sweet and Spicy BBQ Sauce Dip, 1 x Lemon Pepper, 1 x Add Pork Rib Meat (Of-The-Bone!)11.9922.1824.29
2Uber Eats Delivery 86B6C2024-12-1940.123.8143.932024-12-19 18:40:00PickupCompletedPostmates Delivery86B6C-Martin T.NaN+1 312-766-68351CHICKEN WINGS6 pcs1 x Sweet&Spicy glaze (Pairs well w/ LemonPeeper seasoning), 1 x Zesty Lemon Pepper17.9417.9419.64
3Uber Eats Delivery 1B73A2024-12-1932.193.0635.252024-12-19 14:59:00PickupCompletedPostmates Delivery1B73A-Diana O.NaN+1 312-766-68351CIABATTA STEAK SANDWICHRegularNaN19.4519.4521.30
4Uber Eats Delivery 1B73A2024-12-1932.193.0635.252024-12-19 14:59:00PickupCompletedPostmates Delivery1B73A-Diana O.NaN+1 312-766-68351LOADED FRIESRegular1 x Blue Cheese Sauce Drizzle, 1 x Salt and Pepper11.9912.7413.95
5Square Online 18865889552024-12-1963.346.0275.692024-12-19 12:55:00PickupCompletedBELLY RUBB | BBQ Catering | Barbecue To Go and DeliveryRip Geru- Maariplawlavc@gmail.com+172557751661BEEF BACK RIBS (Full Rack)Regular1 x Sweet&Spicy Glaze, 1 x Please, cut it!48.9948.9953.65
6Square Online 18865889552024-12-1963.346.0275.692024-12-19 12:55:00PickupCompletedBELLY RUBB | BBQ Catering | Barbecue To Go and DeliveryRip Geru- Maariplawlavc@gmail.com+172557751661ARTISAN MAC AND CHEESEFull1 x Add gorgonzola! Make it special.12.6014.3515.71
7Postmates Delivery EAA452024-12-1922.061.2023.262024-12-19 11:26:00PickupCompletedPostmates DeliveryEAA45-Anthony S.NaN+1 312-766-68351PINEAPPLE SLAWFullNaN9.469.469.46
8Postmates Delivery EAA452024-12-1922.061.2023.262024-12-19 11:26:00PickupCompletedPostmates DeliveryEAA45-Anthony S.NaN+1 312-766-68351ARTISAN MAC AND CHEESEFullNaN12.6012.6013.80
9DOORDASH2024-12-1857.995.0363.022024-12-18 19:32:00PickupCompletedDoorDashVin Rpoint-of-sale-integration@doordash.com85522281111ARTISAN MAC AND CHEESESide1 x Add gorgonzola! Make it special.7.259.009.78
OrderOrder DateOrder SubtotalOrder Tax TotalOrder TotalFulfillment DateFulfillment TypeFulfillment StatusChannelsRecipient NameRecipient EmailRecipient PhoneItem QuantityItem NameItem VariationItem ModifiersItem PriceItem Options Total PriceItem Total Price
1404DOORDASH2024-01-0520.811.9822.792024-01-05 13:49:00PickupCompletedDoorDashTimothy Lpoint-of-sale-integration@doordash.com85597310401BELLY SLIDERS2 sliders1 x Signature BBQ Sauce9.9810.8311.86
1405DOORDASH2024-01-0435.372.6037.972024-01-04 17:31:00PickupCompletedDoorDashBenjamin Bpoint-of-sale-integration@doordash.com85597310401GRILLED SWEET CORNRegularNaN2.992.993.21
1406DOORDASH2024-01-0435.372.6037.972024-01-04 17:31:00PickupCompletedDoorDashBenjamin Bpoint-of-sale-integration@doordash.com85597310401CRINKLE FRIESSide, Rosemary pepper1 x Sweet and Spicy BBQ Sauce, 1 x Signature BBQ Sauce4.996.697.18
1407DOORDASH2024-01-0435.372.6037.972024-01-04 17:31:00PickupCompletedDoorDashBenjamin Bpoint-of-sale-integration@doordash.com85597310401GET YOUR BABY BACK!Half rack1 x Sweet and Spicy BBQ Sauce, 1 x Signature BBQ Sauce23.9925.6927.58
1408DOORDASH2024-01-0324.842.3627.202024-01-03 14:29:00PickupCompletedDoorDashAlan Spoint-of-sale-integration@doordash.com85597310401GET YOUR BABY BACK!Half rack1 x Signature BBQ Sauce23.9924.8427.20
1409Square Online 8243585682023-12-3036.173.4445.042023-12-30 16:15:00PickupCompletedBELLY RUBB | BBQ Catering | Barbecue To Go and DeliveryNarek Ekmekjyannarek.ek@gmail.com+131066324471GET YOUR BABY BACK!Half rack1 x Pickled peppers, 1 x Signature BBQ Sauce, 1 x Sweet and Spicy BBQ Sauce23.9926.1928.68
1410Square Online 8243585682023-12-3036.173.4445.042023-12-30 16:15:00PickupCompletedBELLY RUBB | BBQ Catering | Barbecue To Go and DeliveryNarek Ekmekjyannarek.ek@gmail.com+131066324471BELLY SLIDERS2 slidersNaN9.989.9810.93
1411DOORDASH2023-12-3053.404.3157.712023-12-30 15:50:00PickupCompletedDoorDashPickUp-Narek Epoint-of-sale-integration@doordash.com85597310401BELLY SLIDERS4 sliders1 x Sweet and Spicy BBQ Sauce, 1 x Signature BBQ Sauce14.5716.2717.58
1412DOORDASH2023-12-3053.404.3157.712023-12-30 15:50:00PickupCompletedDoorDashPickUp-Narek Epoint-of-sale-integration@doordash.com85597310401GET YOUR BABY BACK!Full Rack1 x Sweet and Spicy BBQ Sauce, 1 x Signature BBQ Sauce, 1 x Pickled peppers34.9337.1340.13
1413DOORDASH2023-12-289.340.8910.232023-12-28 13:30:00PickupCompletedDoorDashTest Tpoint-of-sale-integration@doordash.com85597310401CRINKLE FRIESFull, Truffle salt1 x Blue Cheese8.499.3410.23